Sarnoff Corporation

Thomson Licensing

Merck Sharp + Dohme Corp.

Craig Fancourt

Clay Douglas Spence

Lucas Cristobal Parra

Christopher Vincent Alvino

Karim Azer

Matthew Walker, Iii

The present invention uses a microprocessor, a memory storage device, and a segmentation program comprising a plurality of program modules containing computer-readable instructions that cause the microprocessor to measure the spatial offsets between all pairs of scans in an m-mode image of a blood vessel with a cross-correlation function, convert the spatial offsets to a relative wall motion waveform through a global optimization procedure, and then translate the relative wall motion waveform to an absolute wall motion waveform by interpolation over the m-mode image. The resulting detailed absolute wall distension waveform may be beneficially rendered (e.g., superimposed) on the m-mode ultrasound image for display (e.g., on a printer and/or video monitor) and diagnostic purposes, and has enormous potential for enhancing existing techniques for identifying and studying vascular biomarkers, such as vessel wall strain and compliance.

System and method for segmenting M-mode ultrasound images showing blood vessel wall motion over time

A computer system that processes mixtures of signals, such as speech and noise sources derived from multiple simultaneous microphone recordings, in order to separate them into their underlying sources. A source separation routine optimizes a filter structure by minimizing cross powers of multiple output channels while enforcing geometric constraints on the filter response. The geometric constraints enforce desired responses for given locations of the underlying sources, based on the assumption that the sources are localized in space.

Geometric source separation signal processing technique

An iterative approach to vector median filtering wherein the resulting median vector need not be a member of the original data set. The iterative vector median filtering allows for fast convergence for complex computations and an output which is approximate to the mean, particularly for small data sets. In addition, a method and system for registering and matching 2.5 normal maps is provided. Registration of two maps is performed by optimally aligning their normals through 2-D warping in the image plane in conjunction with a 3-D rotation of the normals. Once aligned, the average dot-product serves as a matching metric for automatic target recognition (ATR).

Method and system for filtering, registering, and matching 2.5D normal maps

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Lawrenceville, NJ, United States of America

Craig Fancourt